(2012) addressed only tense mistakes and excluded from the evaluation other kinds of verb errors).

it was assumed that part of the task was solved.


Gold annotation: (i) using gold candidates and verb type vs. automatic; (ii) performance comparison by error type Learning Framework There is a lot of understanding for which algorithmic methods work best for ESL correction tasks, how they compare among themselves, and how they compare to ngram based methods.

despite their intuitive appeal, language models were shown to not work well on these tasks, while the discriminative learning framework has been shown to be superior to other approaches and thus is commonly used for error correction tasks (see Sec.


The increment w ∆ is the difference between the weights for the best active item reaching position i + 1 and the best active item reaching position i.

from now on when we compare the weights for two items x j and x k , with end positions j and k respectively (j < k), then we always add to the score w x j of the first item a fraction of the sum of the increments for the positions between j and k. instead of using w x j when comparing with w x k , we use We call the constant h ∈ [0, 1] the "heuristics factor".


E 2 illustrates the case where the object is an event.

the object of E 2 is E 3 , a BAD-FOR event (overcharging).


Therefore, search queries play two roles simultaneously: in addition to requesting new information, they indirectly convey knowledge in the process.

attributes correspond to quantifiable properties of instances and their classes.


We design our method as a pre-processing procedure so that we can use a well-developed phrasebased system without adding heavy computational complexity to the system.

we propose an abstraction tree that is a shallow and nested representation, i.e., abstraction of the dependency tree as Fig.


It then combines the m-best translation hypotheses of each node to construct a sentence-level translation (line 8-19).

we insert sets of the m-best translation hypotheses of child nodes into the m-best hypotheses of their parent node by replacing the corresponding non-terminals using the cube-pruning (line 17).


If the covering span of an internal node is less than a threshold γ ∈ N, its child nodes are merged (line 3 and 4, Algorithm 3).

we reinsert the child nodes by replacing the corresponding nonterminals with lexical strings that the child nodes have been covered by.


(2) has multiple solutions, word links in these solutions are equally likely, and thus we merge them into a unique solution.

we take the union of the solutions and find the smallest index i l and largest index i r whose elements are 1.


Each node pair has a form Ψ s , Ψ t , A L , where Ψ s ∈ {Ω s , L} n represents the source-side node of length n. It consists of a sequence of lexical words in the source-side vocabulary Ω s and nonterminals L. Ψ t ∈ {Ω t , L} m similarly represents the target-side node of length m. A L preserves correspondences between the non-terminals in the source and target nodes.

we extract a pair of leaf nodes as a pair of lexical strings.


Later, Guevara (2010) and Baroni and Zamparelli (2010) proposed more elaborate methods, in which composition is modeled as matrix-vector multiplication operations.

new to their approach is the proposal to estimate model parameters by minimizing the distance of the composed vectors to corpus-observed phrase vectors.


For adjectives and verbs, we compared the initial model's (λ max ) intercept and the minimum cross-validation error model intercept with corpus-extracted vectors for the corresponding words.

we used the word cooccurrence vector for a verb or an adjective extracted from the corpus and projected onto the reduced feature space (e.g., NMF, 100 dimensions), then computed cosine similarity between this word meaning representation and its corresponding EnetLex matrix initial and minimumerror intercepts, respectively.


Fluency ratings can be carried out without the presence of a reference translation, reducing any remnant bias towards reference translations in the evaluation setup.

we propose a judgment regime in which each task is presented as a two-item fluency and adequacy judgment, evaluated separately, and with adequacy restructured into a monolingual "similarity of meaning" task.


(2013a) where they remove the non-linear hidden layer and there is a single projection layer for the input word.

each current word is used as an input to a log-linear classifier with continuous projection layer and words within a certain range before and after the word are predicted.


We select N by determining the value that optimises the correlation over the training data.

the selection of N and accordingly the best-N languages are based on nested cross-validation over training data, independently of the test data for that iteration of cross-validation.


In order to translate the dataset into a regression task, we calculate the overall compositionality as the number of annotations of entailment for the verb, divided by the total number of verb annotations for that VPC.

following , we only consider the compositionality of the verb component in our experiments (and as such α = 1 in Equation 1).


From the histograms, N = 6, N = 15 and N = 2 are the most commonly selected settings for ENC, EVPC and GNC, respectively.

multiple languages are generally used, but more languages are used for English VPCs than either of the compound noun datasets.


English noun compounds are relatively easy to identify in a corpus, 7 because the components occur sequentially, and the only morphological variation is in noun number (singular vs. plural).

the precision for our token matching method is very high, and the recall is also acceptably high.


3 shows that our model comfortably beats the competitor systems according to all metrics.


In this section, we evaluate measures for estimating human-interpretability at the model level.

for a measure -human-judged or automated -we first aggregate its coherence/interpretability scores for all topics from a given topic model to obtain the topic model's average coherence score.


(2009) (see Section 3), each topic was presented to 8 annotators, with small variations in the displayed topic words (including the intruder word) for each annotator.

each topic has essentially 8 subtly different representations.


We group together all topics for each dataset (without distinguishing the topic models that produce them) and calculate the correlation of one measure against another.

the correlation coefficient is computed for 900 topics/data points in the case of each of WIKI and NEWS.


To better understand the difficulty of the task, we compute the agreement between human annotators by calculating the Pearson correlation coefficient of model precisions produced by randomised sub-group pairs in the topics.

8 for each topic, we randomly split the annotations into two sub-groups, and compute the Pearson correlation coefficient of the model precisions produced by the first sub-group and that of the second subgroup.


To get a better understanding of how well human annotators perform at the task, we compute the one-vs-rest Pearson correlation coefficient using the gold standard annotations.

for each topic, we single out each rating/annotation and compare it to the average of all other ratings/annotations.


All differences between these models are significant (p < 0.01) for both corpora, as computed with bootstrap resampling (Efron and Tibshirani, 1993).

the model ranking on SEMEVAL is replicated on COINCO.


Encouragingly, the margin between full models and baselines is larger than on the complete COINCO and generally amounts to 2-4 % (3.6 % for TFP11).

the full models are more useful on the COINCO corpus than they appeared at first glance; however, their effect still remains much smaller than on SEMEVAL.


A review of the literature shows that theoretical studies on annotator bias are not convergent.

opposite arguments have been proposed concerning Cohen's κ (Di Eugenio and Glass 2004, Arstein and Poesio 2008, Hayes, 2007.


Our results show that α c is systematically outperformed by the other metrics.


1 In contrast to many machine learning approaches that address the problem of paradigm extraction, the current method is intended to produce human-readable output of its generalizations.

the paradigms provided by the system can be inspected for errors by a linguist, and if necessary, corrected and improved.


For example, given the English verb swim, and a simple inflection table swim#swam#swum, 2 we make the assumption that the common sequences sw and m are irrelevant to the inflection, and that by disregarding these strings, we can focus on the segments that vary within the table-in this case the variation i∼a∼u.

we can assume sw and m to be variables that vary from word to word and describe the table swim#swam#swum as x 1 +i+x 2 #x 1 +a+x 2 #x 1 +u+x 2 , where x 1 =sw and x 2 =m in the specific table.


As paradigms are collapsed, we record the information about how the various variables were interpreted prior to collapsing.

for the example in figure 1, we not only store the resulting single paradigm, but also the information that x 1 =r, x 2 =ng in one table and that x 1 =sw, x 2 =m in another.


There is very little language-specific information encoded in the strategy of paradigm generalization that focuses on the LCS in an inflection table.

we do not explicitly prioritize processes like prefixation, suffixation, or left-toright writing systems.


Here, two matches are possible x 1 =b and x 2 =nana and x 1 =ban and x 2 =na.

there are three possible matching 1. shortest match (x 1 =b and x 2 =nana) 2. longest match (x 1 =ban and x 2 =na) 3. try all matching combinations The matching strategy that tends to be successful is somewhat language-dependent: for a language with a preference for suffixation, longest match is typically preferred, while for others shortest match or trying all combinations may be the best choice.


We empirically compare the two approaches by proposing a novel method for type-supervised domain adaptation of a discriminate tagging model, showing that it can be a favourable choice in practical situation.

we split Chinese words into domain-independent and domain-specific categories, and define unlexicalized features for domain-specific words.


Improvements across different automatic evaluation metrics show that the above method is useful when translating negative data.

the entailment-based RTE metric is here used as an alternative to the BLEU score given the semantic and syntactic features assessed, polarity included.


To model some of the interaction between the Giver and the Follower, we additionally consider in our model the previous dialog act of the Follower, which could for example be: Given point-set W and preceding Follower act f , as the giver, we need to determine a procedure for choosing which dialog act g and semantic unit u to produce.

we are interested in the following distribution: which says that, as the Giver, we select our utterances on the basis of what the Follower says, and on the set of waypoints we next wish to describe.


Here we propose the first model to computationally assess if a text deviates from these requirements.

our model predicts the appropriate length for texts based on content types present in a snippet of constant length.


These features use an external source of information about content importance.

we use data commonly employed to develop statistical models for sentence compression (Knight and Marcu, 2002;McDonald, 2006;Galley and McKeown, 2007).


Coreference features are very effective given that they constitute a very small set compared to the other feature sets.

excluding them from the model reduces F 1 scores for TEMPORAL and CONTINGENCY relations by approximately 1% relative to the system that uses all of the features.


Our second goal is to examine the individual aspects of our model and how they impact the overall decision and the latent structure it imposes.

we are interested in understanding the effect that modeling the situated context (pragmatics) of the dialogue has on objection prediction.


This paper proposes syntactic structures whose nodes are enriched with semantic information from statistical classifiers and knowledge from LOD.

yAGO, DBpedia and Word-Net are used to match and generalize constituents from QA pairs: such matches are then used in


Even though we focus on a specific domain, the induction method can be easily translated to other domains.

other life-style domains, such as fashion, cosmetics or home & gardening, show parallels since comparable textual web data are available and similar relation types (e.g.


These relation types are highly relevant for customer advice/product recommendation.

suitsTo and substitutedBy are fairly domainindependent relation types.


(Relation: SubstitutedBy) Since the second ambiguous context involves the two general relation types SuitsTo and Substi-tutedBy, resolving this ambiguity with automatically induced type information has some significance for other domains.

for other life-style domains, domain-specific type information could be obtained following our method from §3.1.


To the best of our knowledge, this paper reports a first study on redundancy detection.

we focus on the task of defining a redundancy measure that estimates the likelihood that a given word or phrase within a sentence might be extraneous.


The knowledge of which word represents what meaning helps in evaluating its contribution.

if a word does not connote any significant meaning, deleting it would not affect the overall sentence; if several words express the same meaning, then deleting some of them might not affect the overall sentence either.


It helps us to compute log Pr(f * |e s,t − , a) by making explicit how each word contributes to words it aligns with.

to compute the probability that f is a translation of e, Pr(f |e), IBM Model 1 defined a generative alignment model where every word f i in f is aligned with exactly one word e a i in e, so that f i and e a i are word level translations of each other.


First, compared to other measures, our proposed model best captures redundancy.

our model picks the correct redundant chunk 21.63% of the time, which is five times higher than the random baseline.


Experimental result suggests that the system tends to achieve higher redundancy detection accuracy when using translations of a language more similar to English.


To confirm this phenomenon, we tried improving the system using Simplified Chinese as the pivot language by merging characters together.

we applied Chinese tokenization (Chang et al., 2008), and then merged alignments accordingly.


The benefits of the pairwise structure and the recursive process are demonstrated by the considerable improvement upon the pipeline counterpart (using the same features, preand post-processing).

the recursive prediction process run on REG events brings about a very substantial improvement (more than 8%).


They are not well suited for reordering or splitting operations.

the syntactic simplification operations that handcrafted systems focus on are out of scope.


The probabilities of concept match have to act as a much weaker filter in our model, and all previous propositions have to be considered as potential landing sites for #19.

we do not know whether a concept match within STM is "good enough", or whether a LTM search is needed.


There is, in this case, a competition between a weak match in STM (the direct vicinity) and a strong match in LTM (further away), which will hopefully result in a successful match between "police" and "law enforcement officer".

we always have to search for matches in both repositories.


This provides us with cost-sensitive online learning algorithms for inducing models from annotated data that take inter-annotator agreement into consideration.

we use online structured perceptron with drop-out, which has previously been applied to POS tagging and is known to be robust across samples and domains (Søgaard, 2013a).


We use a small amounts of doublyannotated Twitter data to estimate F 1-scores and confusion probabilities, and incorporate them during training via a modified loss function.

we use POS annotations made by two annotators on a set of 500 newly sampled tweets to estimate our agreement scores, and train models on existing Twitter data sets (described below).


Cost-sensitive learning takes costs, such as misclassification cost, into consideration.

each instance that is not classified correctly during the learning process may contribute differently to the overall error.


given a matrix S GA ∈ R k×2n−1 resulted by concatenating the node representations h 1 , .


In this paper, we develop attention mechanisms for uncertainty detection.

we generalize standardly used attention mechanisms by introducing external attention and sequence-preserving attention.


In the face of the recent rise of distributed neural representations as a means of capturing lexical meaning in NLP tasks (Collobert et al., 2011;Mikolov et al., 2013a;Pennington et al., 2014), our goal in this paper is to model attribute meaning based on word embeddings.

we use CBOW embeddings of adjectives and nouns (Mikolov et al., 2013a) as underlying word representations and train a compositionality function in order to compute a phrase representation that is predictive of the implicitly conveyed attribute meaning.


The new SLQS variants are on top of the list in many settings.

they perform well in discriminating hypernyms from symmetric relations (antonymy, synonymy, coordination).


The goal of this step is to extract word-feature counts from the input corpus.

we extract three types of features: Dependency Features.


This type of features are based on a trigram model with Kneser-Ney smoothing (Kneser and Ney, 1995).

a word is represented by (1) right and left context words, e.g.


Results of disambiguation can be interpreted by humans as illustrated by Figure 2.

our approach is interpretable at three levels: 1.


• Babelfy (Moro et al., 2014) is a graph-based disambiguation approach which exploits random walks to determine connections between synsets.

babelfy 16 uses random walks with restart (Tong et al., 2006) over babelNet (Navigli and Ponzetto, 2012), a large semantic network integrating Word-Net among other resources such as Wikipedia 13 We used the same word embeddings described in Section 5.1.1 for IMS+emb.


From the result, we find that the performance of two approaches are significantly discrepant at each kind of questions.

iR approach shows noticeable advantages on EQs, while NN approach performs much better on SQs.


(2014) describe answer reranking experiments on YA using a diverse range of lexical, syntactic and discourse features.

they show how discourse information can complement distributed lexical semantic information obtained with a skip-gram model (Mikolov et al., 2013).


For a recent and extensive survey of RNNs in NLP see (Goldberg, 2015).

we use a bi-directional LSTM transducer (Graves, 2012) which outputs a probability distribution over the three possible labels (B, I, and O) per word, taking into account arbitrary length contexts from both past as well as future words.


Here, gender prediction does not benefit from including mental conditions, but helps vice versa.

predicting gender is qualitatively different from predicting mental health conditions: it seems likely that the signals for anxiety ares much more similar to the ones for depression than for, say, being male, and can therefore add to detecting depression.


Intuition Another set of evaluation metrics is inspired by the resemblance of the USF data structure to the typical output of ad-hoc IR systems (Manning et al., 2008;Pound et al., 2010).

each cue word w c can be thought of as an input query issued against some target concept collection V r , where the goal of our association retrieval system is to rank items from the target collection according to their relevance (i.e., their association strength) to the issued query.


1) higher than weakly relevant responses.

nDCG@k the most expressive evaluation metric should be able to distinguish that cue-response pairs such as (lunch, dinner) and (lunch, food) should be ranked higher than weakly associated pairs such as (lunch, box) or (lunch, sandwich).


This paper does not propose a specific approach to assess quality; rather it defines a common ground by providing a so-far-missing holistic view on argumentation quality assessment in natural language.

we first briefly but comprehensively survey all major theories and computational approaches for argumentation quality.


Unlike persuasiveness, which relates to the actual arguments, effectiveness covers all aspects of an argumentation, including the use of language (van Eemeren, 2015).

the three means of persuasion are meant to be realized by what is said and how (Aristotle, 2007).


Often, only one of them will be in the focus of attention in practice, or even only a sub-dimension.

each highlevel dimension has a set of sub-dimensions agreed upon.


Hence, evaluating systems based on performance metrics such as accuracy and F1 scores provides no insight into the differences between the systems and is not able to accurately quantify the similarities between them.

a small difference in accuracy does not necessarily imply a high similarity of the outputs, and, vice versa, a larger difference in accuracy does not necessarily signify vastly dissimilar outputs.


Formally, given a set of tokens T and two accompanying system responses S 1 and S 2 , we quantify how many of the tokens t i ∈ T have a different label in S 1 and S 2 : Note that switching the inequality condition ( =) to equality (=) actually yields the accuracy metric.

taking S 1 as the key and the S 2 as the response and calculating accuracy produces the inverted results of our metric, i.e.


While this answers whether two systems solve a similar or diverse set of problems, it does not enable us to identify the sources of the differences that drive the better performance of one response over the other.

if a token has a different label in S 1 and S 2 , we cannot tell which and if any of the responses is correct.


7 Of the 4.96% different labels in Clevertagger compared to TreeTagger, 58.71% are corrections, 33.13% are new errors, and 8.15% changed errors.

8 one third of the changes that Clevertagger introduces are errors.


We saw that our metric is inversely equivalent to accuracy when taking one system response as the key and the other as the response.

we can calculate the dif f ratio by 1 − , which is equivalent to taking S 1 as the key and S 2 as the response (or vice versa).


The above correlation analysis is a wellestablished practice for automatic metric evaluation, but it is not complete in the sense that it is not meaningful to draw conclusions from it about the differences or similarities between a pair of metrics.

comparing the corresponding correlations relative to each other does not say much since they are both computed on the same dataset, and thus not independent.


We employ the human consensus scores while evaluating the accuracies.

for evaluation, a triplet of descriptions, one reference and two candidate descriptions, is shown to human subjects and they are asked to determine the candidate description that is more similar to the reference.


In our experiments, we use the data 7 provided 7 http://nlp.cs.illinois.edu/ HockenmaierGroup/Papers/VL2016/ HodoshHockenmaier16_BinaryTasks_Data.tar by the authors for a subset of FLICKR-30K (Hodosh et al., 2013).

we consider four different types of distractions for the image descriptions, namely 1) Replace-Scene, 2) Replace-Person, 3) Share-Scene, and 4) Share-Person, which results 15548 correct and distracted caption pairs in total.


Some of these metrics check for synonyms and paraphrases but this approach is still limited to the coverage of the corresponding pair tables.

these metrics do not explicitly evaluate the transfer of meaning in MT.


Moreover, combining them with a metric increases the performance of the metric: 1.9pp for BLEU, 0.9pp for METEOR and 0.6pp for DPM-FComb.

these features can utilize some meaning or style related information which is not captured by the conventional MT evaluation metrics.


Our system is a dependency parser that requires no training, and relies solely on explicit part-ofspeech (POS) constraints that UD imposes.

uD prescribes that trees are single-rooted, and that function words like adpositions, auxiliaries, and determiners are always dependents of content words, while other formalisms might treat them as heads (De Marneffe et al., 2014).


With the emergence of UD as the practical standard for multilingual POS and syntactic dependency annotation, we argue for an approach that takes a fresh angle on both aspects.

we propose a parser that i) requires no training data, and in contrast ii) critically relies on exploiting the UD constraints.


This paper presents a new approach to the problem of cross-lingual dependency parsing, aiming at leveraging training data from different source languages to learn a parser in a target language.

this approach first constructs word vector representations that exploit structural (i.e., dependency-based) contexts but only considering the morpho-syntactic information associated with each word and its contexts.


While previous work have attempted to learn multilingual word clusters or embeddings (Guo et al., 2015) and use these for cross-lingual transfer, this paper explores a different research direction.

we investigate the use of vectorial representations in which lemma information have been abstracted away from both words and contexts, hence reduced to their morpho-syntactic attributes.


The second hypothesis looks at the choice of context types (sequential vs structural) used for learning these embeddings.

we wish to see if the use of syntactic structure delivers better parsing improvements, again in relation to typological similarities or differences.


In this paper, we learn the model w in an online manner with the Passive-Aggressive (PA) algorithm described in (Crammer et al., 2006).

we use the PA-II that uses a squared hinge loss in its predicted-loss cost-sensitive version, in which the cost is computed in terms of the symmetric difference on the edges with respect to the target tree.


In order to define higher-order contexts, we use triplets of delexicalized words centered on each side of the edge.

we concatenate the representations associated with the triplets to keep a tractable model of size 9d 2 .


As to why some languages have better monolingual parsing results than multilingual results, we think this is at least partly due to the lack of flexibility of our model.

morpho-syntactic attributes sets are treated independently from one another making some of them hard to use in the cross-lingual setting.


A recent related task is the SemEval 2016 tweets stance classification (Mohammad et al., 2016).

in its weakly supervised subtask (Task B), no labeled training data was provided for the single assessed topic (Donald Trump).


We enhanced the dataset In this section we propose a model for predicting the stance of a claim c towards a topic sentence t. We assume that c includes a claim target x c , defined as a phrase about which c makes a positive or a negative assertion.

it is defined as the most explicit and direct sentiment target in the claim.


This analysis can be exploited in a purpose of automatic irony detection, which is progressively gaining relevance within sentiment analysis (Maynard and Greenwood, 2014;Ghosh et al., 2015).

it will bring out the most discriminant pragmatic features that need to be taken into account for an accurate irony detection, therefore helping systems improve beyond standard approaches that still heavily rely on features gleaned from the utterance-internal context Gonzalez-Ibanez et al., 2011;Liebrecht et al., 2013;Buschmeier et al., 2014;.


The French and Italian parts of the annotated corpus have been respectively exploited as datasets for the first irony detection shared tasks DEFT@TALN2017 7 and for the SEN-TIPOLC@Evalita shared task on irony detection 8 in both 2014 and 2016 editions (Basile et al., 2014;Barbieri et al., 2016).

currently only the first layer of the annotation scheme has been exploited aiming at detecting if a given tweet is ironic or not.


This issue is challenging, and only recently addressed from computational linguistics.

new data-driven arguments for a possible separation between irony and sarcasm emerged from recent work on Twitter data (Sulis et al., 2016).


I.e., if for sentence s x , MALLET lists the topic t 1 as the topic with the highest probability, then s x is added to the data set of topic t 1 .

the data set of topic t 1 consists of all sentences for which MALLET showed topic t 1 as the most likely topic.


As a special type of agreement, the definiteness of their objects determines which paradigm of the verb is to be chosen.

the form of the verb changes when the definiteness of the object also changes (Törkenczy, 2005 In this way, the feature Definiteness needs to be applied to verbs in Hungarian, moreover, it has a language-specific feature due to the special form triggered by the second person objects.


We treat the keywords, We propose a Natural Language Question Generation (NLQG) model that first encodes the input sequence using some distributed representation and then decodes the output sequence from this encoded representation.

we use a RNN based encoder and decoder recently proposed for language processing tasks by number of groups (Cho et al., 2014;Sutskever et al., 2014).


Table 1 show the statistics of the data.

the DUC-2005 to -2007 data sets not only have very large numbers of sentences and words but also a long target length (the reference summary length) of 250 words.


Their values are comparable with those of ROUGE 1,2 .

f-measure (R 1 ) (single-M) achieved the best Spearman's ρ result.


Recurrent neural networks (RNNs) have been successful for modeling sequences (Elman, 1990).

rNNs equipped with internal short memories, such as long short-term memories (LSTM) (Hochreiter and Schmidhuber, 1997) have achieved a notable success in sequential tasks (Cho et al., 2014;Vinyals et al., 2015).


Our results suggest that a NN model with the shared memory between encoder and decoder is a promising approach for sequence transduction problems such as machine translation and abstractive summarization.

we observe that the attention-based neural machine translation can be further improved by sharedmemory models.


Despite its sophisticated addressing mechanism, the NTM controller does not have mechanism to avoid information collision in the memory.

the NTM controller emits two separate set of access weights (i.e.


Each memory slot vector m t ∈ R k corresponds to the vector representation of information about word w t in memory.

the memory is initialized by the embedding vectors {x t } l t=1 and is evolved over time, through read, compose and write operations.


Our MMA-NSE attention model is similar to the LSTM attention model.

it attends over the premise encoder outputs {h p } T t=1 in respect to the final hypothesis representation h h l and constructs an attentively blended vector of the premise.


Each document in the datasets is associated with human ratings and we used these ratings as gold labels for sentiment classification.

we used the pre-split datasets of (Tang et al., 2015).


On the other hand, all systems tend to show poor results on the IMDB dataset.

the IMDB dataset contains longer documents than the Yelp 13 and it has 10 classes while the Yelp 13 dataset has five classes to distinguish.


has the highest expected reward) is: where Θ a is the set of those parameter values under which the arm a would be selected as the optimal arm: In Thompson Sampling the next arm to pull is sampled according to the posterior probability of the arm being optimal.

an arm a is selected with probability α T,a .


We resort to Gibbs sampling for inference in our model.

we iteratively select a hidden variable and sample a value from its posterior given all the other variables are fixed to their current values.


Across the systems, average performance on individual documents will vary due to variations in the inherent difficulty of each document.

some documents are harder to label than others.


We compare our hierarchical model for capturing the uncertainty over F-measure with the Gaussian distribution.

we associate a Gaussian distribution to each system to model its posterior over the F-measure.


For example, it is often the case that a Yes-No-Question is followed by Reply y or Reply n. To reflect this observation, we create another model, denoted wDA2DA, that has an additional direct connection between the previous DA and the current DA.

equation 8 becomes equation 13: e o e o (y t−1 )+b b b (out) )   As seen in Tables 4 and 5, wDA2DA performs much worse than HA-RNN.


We posit that this happens due to the exposure bias problem (Ranzato et al., 2015).

during training, the model has access to the correct DA of the previous utterance.


Inspired by the work in dialog generation (Li et al., 2016) and spoken language understanding (Hori et al., 2016), we propose the addition of speaker information into the encoder network to explicitly model the interaction patterns of the customer and order-taker.

we learn separate word and role embeddings, and concatenate them to form the input.


This means that the evaluation of the quote→mention stage is a lower-bound.

since a given quote may have multiple mentions that could be considered correct, our system may choose a "wrong" mention for a quote but link it to the correct speaker in the end.


The networks are optimized as described in (Heigold et al., 2016).

the optimization is done with RMSProp (Tieleman and Hinton, 2012), with a fixed initial learning rate and a learning rate decay of two every tenth epoch for German, TIGER, and is adjusted for the other languages according to the amount of training data.


During parsing, the most frequent graph H for the given token is then chosen.

a(i) approximates ← − π (i) by means of the graph most frequently seen among all occurrences of token x i in the training set.


'谢(3)' is the case frame for the sense of 'curtain call'.

case frames are knowledge that solves word sense disambiguation (WSD) by clustering the PASs.


(2013), instead of using all the SRL outputs, we propose to use only automatically generated semantic roles of high quality.

the standard training section of the human-annotated data is used to train a base SRL model (which include three sub-models for predicate sense disambiguation (PD), argument identification (AI) and argument classification (AC)).


However, the focus on large size and wide coverage at entity level has led all these resources to avoid the complementary problem of curating and maintaining a clean taxonomic backbone with as minimal supervision as possible.

no resource, to date, integrates structured information from existing wide-coverage knowledge graphs with empirical evidence from text for the explicit goal of building full-fledged taxonomies consisting of a clean and fully-connected directed acyclic graph (DAG).


For example, bridge:NN:3 is linked to the Babel synset bn:00013077n (the 'infrastructure' sense).

a mapping between the two sense inventories is created to combine them into a new extended sense inventory, a hybrid aligned resource.


Hence, our method is knowledge-based without the need of further supervision other than that contained in KB; • Projection of topological clues from KB back onto the LNKG G on the basis of the links found in the mapping M .

similarly to the case of the reference knowledge base, we do not make any assumption on how the links between G and KB have been created: while there exists different methods to automatically link (lexical) knowledge bases (Navigli and Ponzetto, 2012;, we later show that it is also possible to achieve state-of-the-art performance with a few manually given links; • Propagation of the topological clues across the entire NKG G. to cope with the partial coverage of automatic mappings, as well as the need to reduce the number of manually created KB links, we apply a signal propagation technique that solely relies on the structure of G; • To make use of the resulting topological clues to drive the taxonomy pruning process.


Similarly to the case of the reference knowledge base, we do not make any assumption on how the links between G and KB have been created: while there exists different methods to automatically link (lexical) knowledge bases (Navigli and Ponzetto, 2012;, we later show that it is also possible to achieve state-of-the-art performance with a few manually given links; • Propagation of the topological clues across the entire NKG G. That is, to cope with the partial coverage of automatic mappings, as well as the need to reduce the number of manually created KB links, we apply a signal propagation technique that solely relies on the structure of G; • To make use of the resulting topological clues to drive the taxonomy pruning process.

propagated topological clues from KB are additionally leveraged to ensure that the output ALGORITHM 1: The ContrastMedium algorithm.


Given ground-truth concept granularity judgements, we compute standard accuracy for each of the three classes.

we compare the system outputs against the gold standards and obtain three accuracy measures: one for the root nodes (A R ), one for the nodes 'in the middle' (A M ) and finally one for the leaf nodes (A L ).


Overall, the results show that ContrastMedium leads to competitive performance on a hard, realistic benchmark such as TExEval, achieving the best overall results for both taxonomies.

our algorithm is able to improve the state-of-theart on taxonomy induction by additionally boosting the quality of existing top-performing systems for this task: this is achieved on the basis of a minimally supervised approach that only requires a few links to a reference KB, which is used to provide ground-truth taxonomic relations and guide the cleaning process.


the plots are close to straight lines in a log-log scale).

most entities have only a small number of variants and are mentioned only a few times.


This attribute-conditioned review generation problem is very challenging due to the variety of candidate reviews that satisfy the input attributes.

apart from the given attributes, there are other unknown or latent factors that influence the generated reviews, which renders the generation process non-deterministic.


As shown in Figure 1, our model contains three parts: attribute encoder, sequence decoder, and an attention mechanism.

we first use multilayer perceptrons to encode input attributes into vector representations that are used as latent factors for generating reviews.


Another line of related work is the encoderdecoder model with neural networks.

an encoder is employed to encode input information into vectors, and then a decoder learns to predict results by conditioning outputs on the encoding vectors.


The dataset contains 937, 033 reviews paired with attributes.

we have 80, 256 books, 19, 675 users, and 5 rating levels.


In order to evaluate whether the polarities of generated reviews correspond to their input ratings, we randomly sample some generated reviews and manually annotate their polarity labels.

we regard the rating 1-2 as negative and 4-5 as positive, and then evaluate performance by computing their classification accuracy.


Essentially, given a state s = ([σ|j i], ρ, A) and an input graph C, the Decoder extracts syntactic tree from the graph (cf.

if node i has direct child nodes in C, the descendants of i are shifted (line 6-7) (see Algorithm 3).


economists who read september 's low level of factory job growth as a sign of a slowdown Pipeline september 's low level of factory job growth who as a sign of a slowdown reads economists TBDIL economists who read september 's low level of factory job growth as a sign of a slowdown instance, because of incorrect linearization, there is error propagation to morphological generation in the pipeline system.

economists is linearized to the object part of the sentence and the subject is singular.


PN-Variation does not take the variation into account in the content selection.

this model always chooses the most likely proper name form for the references in the test set which refer to the same person and are described by the same combination of discourse feature values.


In this section we present our parsing model, DENSE, which tries to predict the head of each word in a sentence.

the model takes as input a sentence of length N and outputs N head, dependent arcs.


These consist of a i and a j , the LSTM-based representations for w i and w j (see Equation (4)), and their word and part-ofspeech embeddings, x i and x j (see Equation 3).

we use a trained DENSE model to go through the training corpus and generate features and corresponding dependency labels as training data.


We also assessed the importance of the bidirectional LSTM on its own by replacing our LSTM-based features with those obtained from a feed-forward network.

we used the 1-order-atomic features introduced in Lei et al.


Here we show that noisy-context surprisal as a processing cost model can reproduce effects that were not previously well-explained by either expectation-based or memory-based theories.

we take up the puzzle of structural forgetting effects, where comprehenders seem to forget structural elements of a sentence prefix when predicting the rest of the sentence.


This happens because the probability that an element is erased is independent of the identity of other elements in the sequence, and thus E has no interaction information with any subset of those elements.

this allows us to write: where the variable m ranges over bit-masks of length i − 1, and m I is equal to 1 when all indices I in m are equal to 1, and 0 otherwise.


Information locality can be seen as a decay in the effectiveness of contextual cues for predicting words.

such a decay in cue effectiveness was found to be effective for predicting entropy distributions across sentences in Qian and Jaeger (2012), although that work did not distinguish between an inherent, noise-based decay in cue effectiveness or optimized placement of cues.


A more psychologically accurate model will likely use a more nuanced noise distribution than the simple decay functions in this paper, which do not capture the subtleties of human memory.

simple decay functions to not capture memory retrieval effects of the kind described in Anderson and Schooler (1991), where different items in a sequence have different propensities to be forgotten, in accordance with rational allocation of resources for retrieval.


The proving then proceeds by selecting a subgoal p j (θ j ), searching P for a logical premise p i (θ i ) for which p j and p i and their arguments θ j and θ i are equal (or they unify).

if such a logical premise is found, then the sub-goal is proved and removed from G. the case of the sub-goals g 1 to g 4 in steps 1 and 2 of Figure 1, where predicates match those of p 1 to p 4 and variables unify as x 2 := x 1 and v 2 := v 1 .


We generalize this idea to learn an embedding space that expresses not only the binary relation that phrase x is entailed by phrase y, but also the probability that phrase x is true given phrase y.

we learn a mapping from a phrase The origin (the zero vector) therefore has probability exp(0) = 1.


For any pair x = (x 1 , ..., x N ) and y = (y 1 , ..., y N ), P (X, Y ) ≥ P (X)P (Y ): (Equality holds when x and y are orthogonal, and Therefore, the best we can do for disjoint phrases is learn an embedding that assumes the phrases are independent.

we can map the disjoint phrases to two vectors whose computed joint probability is the product of the individual phrase probabilities.


We also notice that several statistical alignment algorithms, such as IBM Model-1 (Brown et al., 1993), operate under the same data assumptions.

we find that using the translation probabilities learnt by Model-1 as the cross-lingual similarity function (in place of the commonly-used cosine similarity between word embeddings) performs on-par with state-of-the-art cross-lingual embeddings on word alignment and bilingual dictionary induction tasks.


Specifically, we find that using the translation probabilities learnt by Model-1 as the cross-lingual similarity function (in place of the commonly-used cosine similarity between word embeddings) performs on-par with state-of-the-art cross-lingual embeddings on word alignment and bilingual dictionary induction tasks.

as long as the similarity function is based on the sentence ID feature space and the embedding/alignment algorithm itself is not too naïve, the actual difference in performance between different approaches is marginal.


We assume that the same is true for cross-lingual embeddings, and use their recommended settings across all algorithms (where applicable).

we used 500 dimensions for every algorithm, context distribution smoothing with α = 0.75 (applicable to BilBOWA and BWE-SkipGram), the symmetric version of SVD (applicable to Inverted Index), and run iterative algorithms for 100 epochs to ensure convergence (applicable to all algorithms except Inverted Index).


Sentence IDs are not unique to modern embedding methods, and have been used by statistical machine translation from the very beginning.

the Dice coefficient (Och and Ney, 2003), which is often used as a baseline for more sophisticated alignment methods, measures the crosslingual similarity of words according to the number of aligned sentences in which they appeared.


We claim that this metric is mathematically equivalent to the dot-product of two L 1normalized sentence-ID word-vectors, multiplied by 2.

if we use the combination of sentence-ID features and L 1 -normalization to create our word vectors, then for any w s and w t : To demonstrate this claim, let us look at the dotproduct of w s and w t : where i is the index of an aligned sentence.


This paper presents a new, efficient method for learning task-specific word vectors using a variant of the Passive-Aggressive algorithm.

this algorithm learns a word embedding matrix in tandem with the classifier parameters in an online fashion, solving a biconvex constrained optimization at each iteration.


Interestingly, the new objective remains convex, but the margin constraint doesn't as it involves a multiplicative term between the weight vector and the embedding matrix, making the overall problem bi-convex (Gorski et al., 2007).

the problem is convex in w for fixed values of Φ and convex in Φ for fixed values of w. Incidentally, the formulation presented by (Labutov and Lipson, 2013) is also bi-convex (as it also involves a similar multiplicative term), although the authors proceed as if it were jointly convex (i.e., convex in both w and Φ).


In order to better understand and validate the RPA algorithm, we first conducted some synthetic experiments.

we simulated a highdimensional matrix X ∈ R n×p , with n data samples realizing p "words" and p n, using the following generative model: That is, each p-dimensional data point x i was generated from a hidden lower k-dimensional z i and Figure 2: Hyper-plane learned by PA on XΦ (left pane) compared to hyper-plane and data representations learned by RPA (right pane).


9 rand In addition to these pre-trained word representations, we also use random vectors of lengths 50, 100, and 200 as a baseline embedding.

each component of these word vector is uniformly distributed on the unit interval (−1, 1).


It can be observed that DPEMM achieves the best quality of cluster on both Dataset I and Dataset II, whose precision is lower than DPEMM-WE.

on Dataset I, more than 80% of clusters generated by DPEMM has the purity value greater than 0.9, compared to only 70% in LEM.


Though considerable improvement was observed in the precision value, the recall improvement was not significant.

mLN was observed to be more effective for reducing false positives than false negatives.


Evaluation shows consistent improvements across datasets and systems.

when applied to state-of-theart systems, our method yields an absolute improvement in linking performance of up to 1.7 F 1 on AIDA/CoNLL'03 and up to 2.4 F 1 on the English TAC KBP 2015 TEDL dataset.


This yields a binary feature indicating whether a candidate entity is a geographic outlier or not.

since outliers are rare and hence the resulting features sparse, we also also add a feature for the average geographic distanced(d, E) of a candidate entity e to all other entities in document D: d(e, D) = e ∈D\e d(e, e ) |D| − 1 where d(e, e ) is the geographic distance between entities e and e , and |D| is the number of entities 3 high context-similarity due to the appositive race, and an almost perfect string match between mention and Wikipedia title.


Unlike other methods for representing the "aboutness" of a text, such as topic models, entity type statistics are grounded in the KB, thus offering a simple method of measuring the relatedness between entities in terms of their types via the similarity of their type distributions.

we model entity type coherence between a given candidate entity e and all other linked entities in document D as the cosine similarity of the respective type distributions.


We thus implement this constraint in our baseline as a simple post-processing filter on the CRF output.

for probability statement extraction, for a sentence such that |t P | = 0, then we define a modified output y B (B standing here for Baseline) where y B t = O ∀t = 1 : T , else we keep the original output so that y B t = y * t ∀t = 1 : T .


Our chosen method to enforce constraints takes into account current knowledge (i.e., initial decoding from CRF) when estimating probabilities of labels by conditioning posterior probabilities on the presence of label A and B based on current observations.

if only A missing, i.e., |t A | = 0 then we search and we define y C t = A ∀t ∈ t * A and In this method, ϕ represents the the posterior conditional probability given the observed locations of the required labels.


This paper presents a framework to understand negation in positive terms.

we extract positive meaning from negation when the negation cue syntactically modifies a noun or adjective.


This strategy to select potential foci has a few exceptions to avoid foci that yield meaningless interpretations.

we discard potential foci: • whose root has dependency aux, auxpass, cop, poss, dep, prt or punct; • that consist of -the determiner the, a, an, it and there; -the adverbs so, too, though, even, still, as, quite, either, however, anymore, moreover, therefore, furthermore, hence, thus, further, apparently, clearly, specifically, actually, fortunately, and unfortunately; -a single token with part-of-speech tag TO, CC, UH, POS or IN.


We calculate the configuration's redundancy, R 0 , by summing up all the scores.

r 0 is the lexical overlap between sense definitions.


Instead, a path from English to French to English can be used.

we translate a source sentence into a pivot language and then translate the pivot back into the source language.


Throughout our experiments we compare PARANET against a paraphrase model trained with a commonly used Statistical Machine Translation system (SMT), which we henceforth refer to as PARASTAT.

for each language pair used, an equivalent IBM Model 4 phrase-based translation model was trained.


(2015), we next developed a supervised scoring model.

we fit a decision tree regressor on the PPDB 2.0 dataset using the implementation provided in scikit-learn (Pedregosa et al., 2011).


Since evaluation metrics cannot be straightforwardly integrated into this training procedure, we reranked the k-best paraphrases obtained from PARANET using a simple classifier which favors sentences which are dissimilar to the source.

we trained a decision tree regression model with iBLEU as the target variable using the same features described in Section 4.4.


For the first step, instead of just predicting the translation for a single target language, we predict the translation for all languages in the lexicon.

we compute w f i = argmax w∈dict f e (w e i ) cos(v w , context), which is the best translation in language f of source word w e i in language e, given the bilingual lexicon dict f e and the context.


Longer configurations indicate that there is an agreement (regarding the chosen senses) that spans across a longer piece of text.

longer configurations are more likely to provide correct sense choices, since they inherently embed a higher degree of agreement among senses.


During writing of this paper, we have found a new pre-print paper (Jaech et al., 2016) which handles language identification with NN.

they employ Convolutional Neural Networks followed by Recurrent Neural Networks.


Many methods require parallel corpora or comparable corpora to connect the languages (Klementiev et al., 2012;Zou et al., 2013;Hermann and Blunsom, 2013;Chandar A P et al., 2014;Kočiský et al., 2014;Coulmance et al., 2015;Wang et al., 2016), while others use bilingual dictionaries (Mikolov et al., 2013b;Xiao and Guo, 2014;Faruqui and Dyer, 2014;Gouws and Søgaard, 2015;Duong et al., 2016;Ammar et al., 2016), or neither (Miceli Barone, 2016).


In this section, we assess the performance of language models on varying quantities of data, across a number 1,000 10,000 100,000 200 of different source-target language pairs.

we use CLWEs to initialize the first layer in an LSTM recurrent neural network language model and assess how this affects language model performance.


Prior to the identification of repeated nouns in the source text, we tokenize the texts and identify parts-of-speech (POS) using the Stanford NLP tools 3 .

as Chinese texts are not wordsegmented, we first perform this operation and then identify multi-character nouns.


To compute the discourse features, we use the word2vec word vector representations generated from a large corpus (Mikolov et al., 2013), which have been successfully used in the recent past to compute similarity between words (Schnabel et al., 2015).

we employ the model trained on the English Google News corpus 7 with about 100 billion words.


cases when the same noun can be translated into English with two very different words, are less frequent and less semantically divergent than in German.

semantic features are less useful in Chinese because cases of strong polysemy or homonymy seem to be less frequent than in German.


The lower the range values, the more dissimilar the translation pairs T 1 and T 2 , and the better the scores of discourse features.

when the translations are dissimilar, the classifier makes better predictions with the discourse features, i.e.


More recently, Petersen (2007) sought to apply familiar natural language processing techniques to the problem of identifying text difficulty for nonnative readers.

she used a number of parse-based features which captured, for example, the average number of noun and verb phrases per sentence and the height of the parse tree.


To compute F (x), we need to calculate, for each boosting round m, This expression is indicative of a gradient descent step: where ρ m is the step length and To solve a m and w m , we make the basis functions f m (x i ; a, w) correlate most to −g m (x i ), where the gradients are defined over the training data distribution.

using Taylor series expansion, we can get closed form solutions for a m and w m -see Chen and Guestrin (2016) for details.


Our approach is based on the theoretical framework proposed by Johnson and Blair (2006).

we focus on the sufficiency criterion that an argument fulfills if its premises provide enough evidence for accepting or rejecting the claim.


The second most frequent cause of disagreements is due to the length of the arguments.

one annotator annotated remarkably fewer arguments as insufficient.


In our experiments, we also analyzed arguments with respect to the relevance and acceptability criterion.

we conducted several annotation studies with varying guidelines and two annotators on a set of 100 arguments.


We presented a novel approach for assessing the quality of natural language arguments.

we focused on the sufficiency criterion that each logically good argument needs to fulfill.


These distributed representations move beyond unstructured BoW representations by considering the local contexts in which words and phrases appear within documents, which provides a more precise estimate of semantic similarity.

we present two extensions to the initial PV-based method that enable models that learn a common co-embedding space of documents and phrases.


An alternative approach is to treat phrases as 'pseudo-documents' by using the sets of words appearing in the local context of each phrase occurence.

training instances for a phrase's embedding vector are constructed by extracting the local context around each occurrence of a phrase in the document collection.


We use a combination of negative sampling and hierachical softmax via backpropagation (Mnih and Hinton, 2009;Mikolov et al., 2013b).

we use negative sampling and then further optimise the embeddings using hierarchical softmax.


A filter is a sliding window of a given number of words.

different size windows are treated at a time.


To that end, we conduct a multifaceted evaluation in which we compare outputs produced by state-of-the-art neural machine translation and phrase-based machine translation systems for 9 language directions across a number of dimensions.

we measure the similarity of the outputs, their fluency and amount of reordering, the effect of sentence length and performance across different error categories.


Concerning the similarity between the reorderings produced by both MT systems and those in the reference translation, out of 9 directions, in 5 directions the NMT system performs a reordering closer to the reference, in 1 direction the PBMT system performs a reordering closer to the reference and in the remaining 3 directions the differences are not statistically significant.

nMT generally produces reorderings which are closer to the reference translation.


Our experiments reveal that gender markers differ greatly by language, and the specific source language has a significant impact on the features and classification accuracy of the translated text.

gender traits of the original language overshadow those of the target language in both manual and automatic translation products.


Out of these data, 2K and 15K sentence-pairs (for each gender) are held out for tuning and test, respectively, where they comply with the translation direction.

for en-fr experiments, tuning and test sets are sampled from the en-fr direction only and vice-versa.


Gender markers are carried over to (both manual and machine) translations to an extent that overshadows the original markers of the target language.

the markers observed in translated English mirror their original French counterparts, in the same marker role: I (M) in English translations reflecting the original French je (M), say (M) reflecting dire (M), must (F) translated from doit (F) and doivent (F); the latter contradicting the original English must which characterizes M speech.


We rather adjust for the naïve strategy, S naïve , which assigns each document to its own cluster.

we try to measure what is the gain of clustering some documents compared to "doing nothing."


By using the AND function to combine them, we can filter out the cases where using only one representation would result in a false positive.

two documents are grouped only if both their names and their common keywords are similar.


In the computer vision community, the availability of large data sets for object detection and image classification has fueled the development of new architectures.

this made it possible to compare many different architectures and to show the benefit of very deep convolutional networks.


In order to capture relevance, we base PageRank on the reuse of argument units instead.

we construct a graph from all arguments found in web pages.


The original method assigns a high PageRank p(d) to a web page d if d is linked by many other web pages with a high PageRank.

this value corresponds to the probability that a web surfer, who either follows a link on a visited web page or randomly chooses a new page, enters d. based on the link graph induced by a set of web pages D, p(d) is computed recursively as: is the PageRank score of a web page d i ∈ D that links to d, and D i is the set of all web pages that d i links to.


since we expected argument relevance to be perceived subjectively, a total of seven experts from computational linguistics and information retrieval ranked all arguments (in terms of a strict ordering) for each conclusion by how much they contribute to the acceptance or rejection of the conclusion.


The decisive observation is here that the size of the web allows preferring precision over recall.

an initial high-precision, lower-recall argument graph may be obtained by focusing on "low-hanging fruits".


This paper proposes a model to integrate argument relevance in future web search, and it lays theoretical ground for research on argument relevance.

we have defined how to construct an argument graph at web scale as well as how to adapt PageRank for arguments in order to objectively assess relevance given the graph.


In this paper, we address the automatic evaluation of counseling performance by analyzing counselors' language during their interaction with clients.

we present a model towards the automation of Motivational Interviewing (MI) coding, which is the current gold standard to evaluate MI counseling.


The cost function we optimize is: Similar to our entity typing system, we apply CNNs to compute the context representation φ(c).


In this paragraph, we investigate the joint training in more detail.

we evaluate different variants of it by combining relation extraction with other entity typing approaches: EntEmb and FIGMENT.


We analyze semantic changes in loanwords from English that are used in Japanese (Japanese loanwords).

we create word embeddings of English and Japanese and map the Japanese embeddings into the English space so that we can calculate the similarity of each Japanese word and each English word.


Since the word embedding vectors (or simply, embeddings), which have become very popular recently, are powerful tools for dealing with word meanings, we use them to analyze Japanese loanwords.

we create word embeddings of En We often come across advertisements that have extravagant images.


The rest of this section explains the computational approaches to semantic changes or variations of words.

there are mainly two different phenomena, namely diachronic change and geographical variation.


We extracted seed word pairs from an English-Japanese dictionary, edict (Breen, 2000) 12 ; these were used in the minimization problem expressed by Equation 1.

we extracted oneword English entries that were represented as a single Japanese word.


Therefore, if the meaning of a loanword has changed from the original English word, its Dice coefficient should be low.

the cosine similarity should be correlated to the Dice coefficient if the cosine similarity is a good indicator of meaning change.


To see if the obtained word embeddings of English and Japanese can assist in language learn-ing, for purposes such as lexical-choice error correction, we evaluate their usefulness by using the writings of Japanese learners of English.

we use the Lang-8 English data set (Mizumoto et al., 2011) 18 to calculate the Dice coefficient instead of JENAAD.


If a word is often corrected to another, it tends to appear only in the original sentences and not in the corresponding corrections, and thus, its Dice coefficient becomes small, and vice versa.

the Dice coefficient roughly measures how often a word is corrected in the Lang-8 English data.


Deep learning representations have seen their share of successes in Natural Language Processing (NLP) tasks Zheng et al., 2013;Gao et al., 2014;Glorot et al., 2011;.

rNN models have been successfully applied in several scenarios where temporal dependencies provide relevant information (Ian Goodfellow and Courville, 2016;LeCun et al., 2015).


These kinds of multitask architectures are also useful as regularizers (Ian Goodfellow and Courville, 2016).

our cost function J (X, Y ) is defined as follows: where x i represents the i-th sample and y succ and y gen are success and genre labels respectively.


One way to overcome these limitations is to turn to syntactic parse trees and mine them for relevant features unrestricted in size.

we consider tree fragments as features, which are arbitrarily-sized fragments of parse trees.


There is also evidence that, as in spoken language, individuals may shift their style of language in response to the audience.

studies have found that when the expected audience of a tweet is larger, Americans use fewer non-standard and local words (Pavalanathan and Eisenstein, 2015) and Dutch bilingual speakers of a minority language are more likely to use Dutch rather than their other language (Nguyen et al., 2015).


Figure 2: RNNG composition function on each REDUCE operation; the network on the right models the structure on the left (Dyer et al., 2016).

since the RNNG is a generative model, it attempts to maximize p(x, y), the joint distribution of strings and trees, defined as p(x, y) is defined as a product of local probabilities, conditioned on all past actions.


If the endocentric hypothesis is true (that is, the representation of a phrase is built from within depending on its components but independent of explicit category labels), then the nonterminal types should be easily inferred given the endocentrically-composed representation, and that ablating the nonterminal information would not make much difference in performance.

we train a GA-RNNG on unlabeled trees (only bracketings without nonterminal types), denoted U-GA-RNNG.


We conclude that during a derivation, the nodes must be created according to the strict total order < defined above.

for a given tree, there is a unique possible derivation which enforces the constraints described above.


This is a recurring problem for transition-based parsing because it undermines the comparability of derivation scores.

crabbé (2014) observed that the score of a parse item is approximatively linear in the number of previous transitions, which creates a bias towards long derivations.


While the forward algorithm modifies only two arcs at once, the backward algorithm may modify more than two arcs considering possible parse errors at prediction.

when we find a target node having multiple children, we change the head of all these children to the head of the target (excluding those with the mwe label) 2 .


Although this additional complexity seems deficiency, the oveall scores (FAS) increase, which suggests that the majority case is successful predictions of both arcs thanks to our conversion.

though our method slightly drops scores of functional arcs, it saves much more arcs of core relations, which are generally harder.


Figure 3 below shows a negative correlation, which means the reduction of entropy as a whole by the conversion relates with the overall improvement.

in MST, we find a strong negative correlation (r = −.75; p < .01).


For example, the top-3 nearest neighbours for superior in CR are exceptional, excellent and unmatched for WN, while for the baseline, they are inferior, exceptional and excellent.

similar to the intuition behind counter-fitting, the methods appears to learn to differentiate between synonyms and antonyms, in a manner which is sensitised to the target domain.


The massive amount of text provided by users of social media like Facebook and Twitter give researchers the opportunity to investigate topics that were not previously tangible.

the study of economic outcomes has been turning to the use of social media data in order capture non-traditional factors like consumer mood.


We focus on two outcomes per county, foreclosure and increased price rates (zillow website, 2016), and consider a wide variety of traditional socioeconomic and demographic predictors to compare.

socioeconomic variables include median income, unemployment rate and percentage with bachelors degrees while demographic variables include median age; percentage: female, black, hispanic, foreign born, married; and population density.


All models except the 4th are learned via L2 penalized ("ridge") regression (Goeman et al., 2016 Residualized Control Approach In order to effectively exploit Twitter language in our model, we suspect that we need to treat the language features (which are numerous, noisy, more biased, and non-normal) differently than the control variables (which are few, mostly unbiased, and mostly normal).

simply combining the two may lead to losing the importance of the controls amongst the numerous features.


The objective is to optimise ROUGE-1 by maximising the number of word tokens paired up between system and reference summaries.

one is trying to choose the sentences, within budget, that cumulatively maximise the number of unigram tokens that can be paired with those of reference summaries.


Suppose further that each vertex s ∈ V is associated with a weight w v .

the max k -weighted dominating set problem consists in determining a subset of vertices of total weight k that are adjacent with the maximum number of vertices in G. if we set w v = 1 for each vertex, then the two problems are identical, showing the corresponding NP-hardness of this weighted version of the problem.


We show that in all corpora except SHER-LOCK, negation scope is most often delimited by punctuation.

in these corpora, examples like (2) outnumber those like (1).


Combining geographic and textual contexts to train embeddings leads to better translation performance than using textual or geospatial contexts in isolation.

with a seed dictionary of just 528 Turkish words and monolingual text of just 236k tweets, our supervised method is able to predict correct translation pairs with 67.8% precision.


Table 2 shows a set of expected projections along with how often the expectations are met across the PCEDT.

we calculate the percentage of cases when an English word with a particular PTB tag has the expected Czech tag projected onto it.


Now, given the results in Jaeger (2010), it seems straightforward to assume that speakers also exploit this kind of variation in order to optimize the surprisal profiles of their utterances.

speakers are expected to prefer overt articles if they precede nouns with rather high surprisal, and to prefer article omission, if they precede nouns with rather low surprisal (in order to raise the surprisal on the noun and to distribute the information encoded more uniformly across the utterance).


In contrast, the gradient descent operated by perceptron-like algorithms does not allow us to estimate how much our solution is far away from the optimum.

we do not know at which epoch our algorithm should stop.


It should be noted that implementing a sound comparison was rather complex as it required testing all the algorithms in the same conditions and optimally setting their parameters.

lSSVM and lSP adopt different graph models and use different methods to extract spanning trees from a document graph, namely, Kruskal's (Kruskal, 1956) and Edmonds' (Chu and liu, 1965;Edmonds, 1967).


the induced meta-learning for analyzing what such characteristics are predictive of gains.

for each task considered, we extract a number of dataset-inherent features (see Table 2) as well as features that we derive from the learning curve of the respective single-task model.


The pattern is that if the main tasks have flattening learning curves (small negative gradients) in the 20-30% percentile, but the auxiliary task curves are still relatively steep, MTL is more likely to work.

multi-task gains are more likely for target tasks that quickly plateau with non-plateauing auxiliary tasks.


The difference is that in EPCA, the representations wj are learned jointly with C. 5 In the general form of EPCA, that column is passed through some "inverse link" function to obtain the expected feature values under the distribution, which in turn determines EPCA allows us to suppose that each x j was drawn from a multinomial-a more appropriate family for drawing a count vector.

our observation is that skip-gram is precisely multinomial EPCA with the canonical link function (Mohamed, 2011), which generates x j from a multinomial with log-linear parameterization.skipgram chooses embeddings C, W to maximize This is the log-likelihood (plus a constant) if we assume that for each word j, the context vector x j was drawn from a multinomial with natural parameter vector C w j and count parameter N j = i X ij .


We present initial studies of a novel method which combines utterances with user metadata.

we develop an ensemble of different machine learning techniques on different subsets of data to improve child detection.


Age and gender identification from speech is not a new problem and much research has been done in this area (sec.2), yet the results are far from perfect.

the task to identify adults from kids becomes more challenging when the utterances are only a couple of seconds long.


We investigate a novel multimodel approach to improve classifier accuracy by combining speech data with rich usage metadata (sec.3).

we extract features separately from speech and usage data, and build individual models that are fused together (sec.4) to improve classifier performance.


We train the Marmot (Müller et al., 2013) using features derived from the segmentation.

we create a binary feature that fires on each segment in the training data.


Supervised systems may be particularly sensitive to this issue (Daumé III, 2007), and therefore training data should be clustered accordingly.

two recent studies found that clustering training data was essential for distributional hypernym discovery systems to perform accurately (Fu et al., 2014;Espinosa-Anke et al., 2016).


Our results are consistent with their claims, not only for the direct models which are similar to the models they used but also for those pivoting through English to Arabic.

we find the correlation between the word-per-sentence 3 in X to correlate with En→X and Ar→En→X BLEU by r = 0.82 and r = 0.91, respectively.


Finally, we showed that even if the dictionaries are "translated" from the English Wiktionary through a small list of bilingual word pairs we can still predict what POS taggers are best for 11/20 languages.

we found that it is sufficient to translate a small list of frequent words from English to start reliably evaluating crosslingual taggers for the true targets.


These two additional blocks of features have been selected following the definition of the CTs in the annotation guidelines.

the presence of temporal expressions in a clause can facilitate the distinction between the NARRATIVE and the DESCRIPTIVE classes, while the polarity features should facilitate the identification of the EVALUATIVE class.


Table 2 demonstrates that the character models are superior to the word models.

we found that models which employ character level n-grams appear to be more suitable for datasets with a large number of authors, i.e.


The challenge in structured prediction largely stems from the size of the output space.

in NLP, for sequence labeling (e.g., named entity recognition), which is the first building block of our system, a number of different methods have been proposed, namely CRFs (Lafferty et al., 2001), Maximum Margin Markov Network (M 3 N) (Taskar et al., 2003), SVM struct (Tsochantaridis et al., 2004) and SEARN (Daumé III et al., 2009).


The output is then used for the next and final step (3) of constructing the prop-erty tree.

we construct a fully connected directed graph G = {V, E} with the entities as nodes V , and edges E representing the part-of relation with the respective classifier scores as weights.


Overall, the CRF performs well with a score of F 1 = 0.80.

space is the best performing entity type.


From WikiData the system can extract properties for the named entities found in the article.

it looks for capitals (for countries), countries (for cities or regions) and demonyms (for all the geographical locations), while for people it extracts names, surnames, occupations and fields of work.


These constraints are enforced to ensure that the rhythmic properties of the lyrics keep unchanged.

keeping the count of syllables constant means that the synthesizer should be able to sing the word at the same pace of the original, while the rhyme at the end of a song line is maintained to avoid disrupting rhyming with other line endings.


Parsing Models Our parsing model is based on transition-based dependency parsing paradigm (Nivre, 2008).

we use an arc-eager transition system (Nivre, 2003).


As a consequence, speakers can reliably answer questions involving quantifiers even in contexts that preclude counting (Pietroski et al., 2009), as well as children lacking exact cardinality concepts can understand and appropriately use quantifiers in grounded contexts (Halberda et al., 2008;Barner et al., 2009).

knowledge about (large) precise numbers is neither necessary nor sufficient for learning the meaning of quantifiers.


we focus on those cases where both quantification strategies might be used, namely scenes containing target (dogs) and distractor objects (cats).


The former, ranging from -1 to 1, evaluates the similarity between two vectors with respect to their orientation and irrespectively of their magnitudes.

the more two vectors are overall similar, the closer they are.


As can be seen from the boxplots, both Qs and Cs are ordered on a scale.

cosine distance is highest in no scenarios (where the target is not present), lowest in all scenarios.


The third is a similar neural network that approximates to 1 the   Table 2, nn-cos is overall the best model for Qs, whereas nn-dot is the best model for Cs.

mean average precision (mAP) is higher in nn-cos for 3 out of 4 Qs, with only most reaching slightly better mAP in Q nn-dot due to the high number of cases confounded with all by the Q nn-cos model (see Table 3).


While dependency parsers reach very high overall accuracy, some dependency relations are much harder than others.

dependency parsers perform poorly in coordination construction (i.e., correctly attaching the conj relation).


With regard to resources required to construct each gold standard, a single DA data collection run cost USD$109 on average , while the cost estimate provided to us by a professional post-editor for the same test set came between USD$1,422 and USD$2,728.

the cost of producing the gold standard is 10-20 times greater for postediting than DA.


On closer look, however, we find that the current F1-based results may be somewhat misleading.

several papers report F1 scores in the higher 60% level on balanced datasets-on such datasets a baseline that predicts each pair to be in the hypernym relation already achieves 66% F1.


We show how the Baroni dataset outperforms the other datasets.

we find that models trained on Baroni's data can outperform other models even on their home turf.


To obtain a distributed representation for each word w ∈ V, we employed the word2vec model (Mikolov et al., 2013).

for our experiments, we used a publicly available model 1 M consisting of 300-dimensional vectors trained on a Google News dataset of about 100 billion words.


To generate a representation for each document, we assume that its words were generated by a multivariate Gaussian distribution.

we regard the embeddings of all words w present in a document as i.i.d.


As regards the proposed method, we also computed the sensitivity of the classification to the value of parameter α.

figure 1 shows how the classification accuracy changes with respect to parameter α on the TREC dataset.


The model is trained to give two kinds of outputs to the word class layer (v t ) as well as to the output word layer (w t+1 ) (Mikolov et al., 2011).

it produces the posterior probability P (v t |s t ) of the word class v t given the current state s t , and the posterior probability P (w t+1 |v t , s t ) of the next word w t+1 given the current word class and LSTM state.


While very useful, those tools typically do not offer extra features beyond screen sharing or instant messaging.

they broadcast participant voices without leveraging the rich information conveyed in speech.


(2016a) to select nodes from G (i.e., to extract tokens from the text), we frame the keyword identification problem as the maximization of a set function under a budget constraint.

we define a keyword quality function f that not only measures the cumulative CoreRank score of a given set of terms S, but also the density of the subgraph they induce: where λ is a trade-off parameter, and the set function h counts the number of edges that should be added to the subgraph induced by S to make it complete: where |S|, resp.


In these conditions, the language modeling and ordering assumptions of most MT models are unwarranted, so we instead use a simple bag-of-words translation model based only on co-occurrence: IBM Model 1 (Brown et al., 1993) with a Dirichlet prior over translation distributions, as learned by fast align (Dyer et al., 2013).

2 for each pseudoterm, we learn a translation distribution over possible target words.


Moreover, machine learning techniques have been used to train negotiation strategies from data, in particular reinforcement learning (RL) (Georgila and Traum, 2011;Efstathiou and Lemon, 2015;Keizer et al., 2015).

it has been shown that RL dialogue agents can be trained to strategically select offers in trading dialogues (Keizer et al., 2015;Cuayahuitl et al., 2015c), but also to bluff and lie (Efstathiou and Lemon, 2015;Efstathiou and Lemon, 2014).


This setting can be actually explained by that the output values of the two functions are between 0 to 1, which is used to decrease the learning rate.

the learning rate of the two functions needs to be set a larger value than the baseline in order to be decreased by the two functions.


Multilingual synsets allow us to translate the triples in KB s into any of the languages covered by BabelNet.

we can translate source language triples (e s 1 , r, e s 2 ) into the corresponding target language triples (e t 1 , r, e t 2 ) for each target language.


Our results indicate that using cross-lingual links between entity lexicalizations in different languages yields better NTNKBC model.

our experiments imply that the cross-lingual signal enabled through the multilingual KB and shared multilingual embedding space provides improved regularization for the neural KBC model.


We propose to use the attention-based encoderdecoder for multi-target stance classification.

we regard the given tweet as the input, and the model is trained to generate the stance labels for targets.


We investigate an attention model to this end.

a vanilla LSTM model is used to induce an attention value of the whole sentence.


To address this, we utilize the attention mechanism to calculate the contribution of each word towards targeted sentiment classes, as shown in Figure 1, where the gray level in the spectrum means the contribution of words.

we build a vanilla model using a bidirectional LSTM to extract word embeddings over the sentence and then apply attention over the hidden nodes to estimate the importance of each word.


Attention is applied on left and right contexts, respectively.

the representation of the left context is: and the representation of the right context is: Together with the vanilla representation s, the distribution of sentiment labels is predicted by: A second extension is to add gates to control the flow of context information (BILSTM-ATT-G).


We compare the performances of various models against OOV rates.

we split the test sentences into two sets, where one contains sentences that have no OOV and the other consist of sentences which have at least one OOV.


We propose to identify purchase stages from Twitter data following the AIDA model (Awareness, Interest, Desire, Action).

we define the task of classifying the purchase stage of each tweet in a user's tweet sequence.


Next, we show the positive impact of GRU as a tweet sequence model by replacing it with models that do not use sequential information.

we use a simple feed-forward (FF) model    Table 6: Impact of ranking layer on RCRNN (with and without a hidden layer) to predict the output label given only the current tweet representation calculated by a CNN.


We found that GBMT ctx is better than PBMT across all test sets.

the improvements are +2.0/+0.7 BLEU on average on ZH-EN and DE-EN, respectively.


Increasing the training material increases performance for all test sets, 3 but even a small training set is enough to improve upon native approaches.

using 200 training instances already yields a @1 of 36.6 on Wiki >25 , while the best native tops at 20.


The objective of bilingual lexicon induction is to find translation pairs between two languages.

we aim to pair each word in the source vocabulary with its translation in the target vocabulary.


When examining the translation of the determiner su, the comparison of the first two confusion matrices in Table 2 shows that CM translates su more poorly than BL.

it misses many occurrences of su that should have been translated as its, rendering them generally by his.


Tree depth is defined as the maximum number of nodes between the root and a leaf node; the width is the maximum number of siblings at any of the levels of the tree; and the ramification factor is the mean num-ber of children per level.

the tree features characterize the complexity of the dependency structure of the sentences.


The main difficulty the classifier has is distinguishing background sentences from objective sentences.

a third of the objective sentences are incor-  Table 6: Confusion matrix on PubMed 20k RCT obtained with our model.


The mean rating for the textual labels is 1.57 with a variance of 0.29, while the mean rating for the image labels is 1.84 with a variance of 0.51.

the image labels are, on average, better quality, but there is equally more variability among the image labels.


However, when we share the latent representations for the last few layers (joint-NN), we see that results improve substantially.

ly, textual label performance is on par with baseline, suggesting the addition of image label data helps learn the latent representations of textual labels.


In addition, we propose an integrated method to train on both F-Score and label accuracy.

we make contributions as follows: • We propose a method to directly train on F-Score rather than label accuracy.


h is constructed by concatenating word and user embeddings in the order shown in Figure 1, defined as where v s , v j and v r are embeddings of the sender, content words and recipients, respectively.

embeddings from input words are summed dimension-wise to the project layer, just like in the CBOW structure.


To understand if the learned user embeddings reflect actual email correspondence, we study the relation between the similarity of users' embeddings and the frequency they communicate.

for each target user u i , we first identify all others {u j | j =i } that he/she has had communications with, and then bucket the cosine similarity between their embeddings into intervals.


Thus, to reduce the number of candidate pairs, we transformed the 2-   .

instead of considering all permutations of entities within a sentence, we consider all combinations of entities from left to right, changing when necessary the contains relations into is-contained relations.


Directed graphical models provide a natural formalism for principal parts morphology since a graph topology can represent relations between inflected forms and principal parts.

we apply string-valued graphical models (Dreyer and Eisner, 2009; to the problem.


The question of intellectual attribution is important for researchers: not understanding the argumentative status of part of the text is a common problem for nonexperts reading highly specific texts aimed at experts (Rowley, 1982).

after reading an article, researchers need to know who holds the "knowledge claim" for a certain fact that interests them.


These techniques take a strategy that maximize the margin between critical examples and the separating hyperplane.

compared with other conventional statistical learning algorithms, SVMs achieve high generalization even with training data of a very high dimension.


Let us consider two hyperplanes called separating hyperplanes: (2) (3) can be written in one formula as: Distance from the separating hyperplane to the point xi can be written as: Thus, the margin between two separating hyperplanes can be written as: w,b;xi) xi ;Yi = l xi;yi=--i  To maximize this margin, we should minimize Hwll.

this problem becomes equivalent to solving the following optimization problem: Minimize: Furthermore, this optimization problem can be rewritten into the dual form problem: Find the Lagrange multipliers c~i >_ O(i = 1,..., l) so that: Subject to: In this dual form problem, xi with non-zero ai is called a Support Vector.


This result supports our intuition that we need a combination of at least two features.

it will be hard to confirm a dependency relation with only the features of the modifier or the modfiee.


It is possible to perform topic analysis by combining an existing word extraction method (e.g., tf-idf) and an existing text seg-mentation method (e.g., TextTiling).

one can extract key words from a text using tf-idf, view these extracted key words as topics, segment the text into blocks using TextTiling, and estimate the distribution of topics (key words) within each block.


erode officers and businessmen said ~ou~h curbs against Japan might allen hard-hit producers o~ anuLicondnctors in third ... block 2 ........ Hong-|en$(0.16) trado-export-ta~iff-impert(O.

10) U5(0.06) 15 In Hung long, where nauspaporo have alleged Japan has been nailing baler-cost semiconductors, some electronicsmanu~acturnrn share ... 16 "a very short-term vies."


A third difference is that the Supertags in his converted data do not always fit together, due to the discrepancy between the XTAG grammar and the Treebank annotation and the fact that the XTAG grammar does not cover all the templates in the Treebank (see Section 4.1).

even if the Supertagger is 100% accurate, it is possible that the correct parse for a sentence can not be produced by combining those Supertags in the sentence.


This paper presents results for a maximumentropy-based part of speech tagger, which achieves superior performance principally by enriching the information sources used for tagging.

we get improved results by incorporating these features: (i) more extensive treatment of capitalization for unknown words; (ii) features for the disambiguation of the tense forms of verbs; (iii) features for disambiguating particles from prepositions and adverbs.


The constraints restrict the model to behave in accordance with a set of statistics collected from the training data.

the statistics are expressed as the expected values of appropriate functions defined on the contexts h and tags t. the constraints demand that the expectations of the features for the model match the empirical expectations of the •features over the training data.


One way of representing a context is statistical language nmdels which provide a word sequence probability, P(w~), where w~ denotes the sequence wi...wj.

they provide the conditional probability of a word given with the previous word sequence, P( wilw~-l ), which shows the prediction of a word in a given context.


Context co-occurrence vectors are expected to have the sinfilar property.

if a word tends to appear in a given context, the word co-occurrence vector of the word and the context co-occurrence vector of the context will point in similar directions ......


Topic-based models capture long-distance lexical dependencies via intermediate topics.

the estimated distribution of topics, P(t]w~), is the representation of a context.


The languagespecific features detect errors better.

matched multi-character words are usually correct.


Invert the lexicon to make it an English-Chinese lexicon.

for each English word e, we associate it with a list of Chinese words cl, c2, ... Cm together with non-zero translation probabilities P( elc~).


Also, a document matching different translations of one term in the original query may be ranked higher than a document that matches translations of different terms in the original query.

a document that contains terms at, a2 and a3 may be ranked higher than a document which contains terms at and bl.


To get an upper bound on performance of any disambiguation technique, we manually disambiguated the Trec5C-medium, Trec6Cmedium and Trec4S queries.

for each English query term, a native Chinese or Spanish speaker scanned the list of translations in the bilingual lexicon and kept one translation deemed to be the best for the English term and discarded the rest.


We can infer that the associated word list of the query term contains the terms that are the most possible components of a compositional phrase.

the phrase information can be kept by this way.


To make the training task more manageable, it is common practice to map the space of all terms into a lower dimensional feature space.

instead of estimating a different A for each term in the vocabulary, we can model A as a function of tf and idf and various other features of   (t, rel,tf) tEbin Similarly, the denominator can be approximated as: where: D (description), E (query expansion) documents.


The estimation method starts with a training file which indicates, among other things, the number of relevant and irrelevant documents for each term t in each training query, q.

for each t and q, we are are given dr (t, rel, tfo) and dr (t, tel, tfo), where dr(t, tel, tfo) is the number of relevant documents d with tf(t, d) = tfo, and df (t, rel, tfo) is the number of irrelevant documents d with tf(t, d) = tfo.


One could simply use the ks in table 2 as is.

when we see a new term in the test material, we find the closest bin in table 2 and report the corresponding ~ value.


The table shows the slopes and intercepts for ten regressions, one for each combination of tf and B (B = 1 iff avtf is large.


While finding the web pages that contain the desired information is an important first step, what an information seeker needs is often an answer to a question.

given a question, we want a system to return the exact answers to the question, and not just the documents to allow us to further search for the The need for question answering (QA) systems has prompted the initiation of the question answering track in TREC-8 (Voorhees and Tice, 2000) to address this problem.


To evaluate our learning approach, we trained AQUAREA$ on the same development set of stories and tested it on the same test set of stories as those used in all past work on the reading comprehension task (Hirschman et al., 1999;Charniak et al., 2000;Riloffand Thelen, 2000;Wang et al., 2000).

the set of stories used are published by Remedia Publicatious.


The WordNet hypernym relations, however, do not form a tree, but a DAG, so whenever there are multiple hypernyms, the uniformly-weighted mean is taken of the probabilities conditioning on each of the hypernyms.

p (Sm, I t~,,mi, @3(Sh)  Note that in the first level of back-off, we no longer condition on the head word, but strictly on its synset, and thereafter on hypernyms of that synset; these models, then, get at the heart of our approach, which is to abstract away from lexical head relations, and move to the more general lexico-semantic relations, here represented by synset relations.


We went through all 220k words of the corpora, synchronizing them.

we made sure that the corpora were identical up to the spelling of individual tokens, correcting all 8We aim to investigate the precise effects of our back-off strategy in the next version of our combined parsing/WSD model.


This correcton task ranged from the simple, such as connecting two sentences in one corpus that were erroneously broken, to the middling, such as joining two tokens in Sem-Cor that comprised a hyphenate in Brown, to the difficult, such as correcting egregious parse annotation errors, or annotating entire sentences that were omitted from SemCor.

the case of hyphenates was quite frequent, as it was the default in SemCor to split up all such words and assign them their individual word senses (synsets).


Sentence patterns or pattern ruels specify the sub-structures of the sentences.

segmentation positions are determined in view of the global sentence structure.


A sentence can be segmented into shorter segments that correspond to the constituents of the sentence.

segments correspond to the nonterminal symbols of the context-free grammar 1.


It has been used in its most classical setting (Duda and Hart, 1973).

assuming independence of features, it classifies a new example by assigning the class that maximizes the conditional probability of the class given the observed sequence of features of that example.


• Surprisingly, LB in A+B-A (or A+B-B) does not achieve substantial improvement to the results of A-A (or B-S) win fact, the first variation is not statistically significant and the second is only slightly significant.

the addition of extra examples from another domain does not necessarily contribute to improve the results on the original corpus.


Regarding portability, we observe a significant accuracy decrease of 7 and 5 points from A-A to B-A, and from B-B to A-B, respectively 9.

even when the sazne distribution of senses is conserved between training and test examples, the portability of the supervised WSD systems is not guaranteed.


Currently, the query component does not yet process all possible expressions in the query language.

it does not allow disjunctions and it does not allow to query for tokens.


Other atomic queries combined with with negations and conjunctions are possible.

complex syntac-tic structures involving category and edge labels and binary relations can be searched.